CN116579874A - Edible fungus planting environment intelligent management and control system based on artificial intelligence - Google Patents
Edible fungus planting environment intelligent management and control system based on artificial intelligence Download PDFInfo
- Publication number
- CN116579874A CN116579874A CN202310592477.2A CN202310592477A CN116579874A CN 116579874 A CN116579874 A CN 116579874A CN 202310592477 A CN202310592477 A CN 202310592477A CN 116579874 A CN116579874 A CN 116579874A
- Authority
- CN
- China
- Prior art keywords
- regulation
- control
- analysis
- equipment
- item
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 241000233866 Fungi Species 0.000 title claims abstract description 55
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 15
- 238000004458 analytical method Methods 0.000 claims abstract description 185
- 230000002159 abnormal effect Effects 0.000 claims abstract description 87
- 238000001514 detection method Methods 0.000 claims abstract description 26
- 238000005192 partition Methods 0.000 claims abstract description 15
- 238000013441 quality evaluation Methods 0.000 claims abstract description 13
- 238000012795 verification Methods 0.000 claims abstract description 6
- 238000012544 monitoring process Methods 0.000 claims description 81
- 238000011156 evaluation Methods 0.000 claims description 64
- 238000000034 method Methods 0.000 claims description 25
- 238000004364 calculation method Methods 0.000 claims description 23
- 238000011897 real-time detection Methods 0.000 claims description 23
- 230000007613 environmental effect Effects 0.000 claims description 21
- 230000015556 catabolic process Effects 0.000 claims description 20
- 238000006731 degradation reaction Methods 0.000 claims description 20
- 230000008569 process Effects 0.000 claims description 19
- 238000006243 chemical reaction Methods 0.000 claims description 12
- 230000004044 response Effects 0.000 claims description 10
- 238000004904 shortening Methods 0.000 claims description 9
- 238000012797 qualification Methods 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 2
- 238000011009 performance qualification Methods 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 11
- 238000012423 maintenance Methods 0.000 abstract description 4
- 230000008030 elimination Effects 0.000 abstract description 3
- 238000003379 elimination reaction Methods 0.000 abstract description 3
- 210000002686 mushroom body Anatomy 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 235000001674 Agaricus brunnescens Nutrition 0.000 description 1
- 240000006499 Flammulina velutipes Species 0.000 description 1
- 235000016640 Flammulina velutipes Nutrition 0.000 description 1
- 240000008397 Ganoderma lucidum Species 0.000 description 1
- 235000001637 Ganoderma lucidum Nutrition 0.000 description 1
- 206010067482 No adverse event Diseases 0.000 description 1
- 240000001462 Pleurotus ostreatus Species 0.000 description 1
- 235000001603 Pleurotus ostreatus Nutrition 0.000 description 1
- 244000197580 Poria cocos Species 0.000 description 1
- 235000008599 Poria cocos Nutrition 0.000 description 1
- 208000006011 Stroke Diseases 0.000 description 1
- 241001506047 Tremella Species 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 235000013311 vegetables Nutrition 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y10/00—Economic sectors
- G16Y10/05—Agriculture
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/10—Detection; Monitoring
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- General Business, Economics & Management (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Entrepreneurship & Innovation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- Agronomy & Crop Science (AREA)
- Tourism & Hospitality (AREA)
- Primary Health Care (AREA)
- Animal Husbandry (AREA)
- Health & Medical Sciences (AREA)
- Marine Sciences & Fisheries (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Mining & Mineral Resources (AREA)
- Apparatus Associated With Microorganisms And Enzymes (AREA)
Abstract
The invention belongs to the technical field of edible fungus planting management and control, in particular to an edible fungus planting environment intelligent management and control system based on artificial intelligence, which comprises a server, an environment data partition verification module, an equipment regulation and control supervision analysis module, an equipment performance excellence analysis module and an environment management and control quality evaluation module; according to the invention, the environment partition detection feedback is carried out on the edible fungus planting area, so that the corresponding environment management and control project is marked as a normal project or an abnormal project, the regulation and control equipment corresponding to the abnormal project is subjected to supervision analysis, the regulation and control effect analysis and the performance analysis of the corresponding regulation and control equipment are effectively combined, the elimination and replacement of the corresponding regulation and control equipment are timely carried out, the subsequent corresponding regulation and control effect and regulation and control efficiency are ensured, the environment management and control quality evaluation is carried out through the environment management and control quality evaluation module, the subsequent enhancement of the environment supervision of the corresponding edible fungus planting area and the maintenance and overhaul of the corresponding regulation and control equipment are facilitated, and the growth situation of edible fungi is ensured.
Description
Technical Field
The invention relates to the technical field of edible fungus planting management and control, in particular to an intelligent management and control system for edible fungus planting environments based on artificial intelligence.
Background
Edible fungi refer to edible higher fungi with large fruiting bodies, edible fungi are divided into narrow-definition edible fungi and broad-sense edible fungi, the narrow-definition edible fungi refer to fungi with edible mushroom bodies, such as mushrooms, flammulina velutipes, oyster mushrooms, edible fungi, tremella and the like which can be eaten as vegetables or eaten raw, the broad-sense edible fungi comprise fungi with edible mushroom bodies or edible mushroom bodies, but can be used as health care foods, and the fungi with no adverse reaction to human bodies, such as fungi with the shape of a mushroom body, such as ganoderma lucidum, poria cocos, corious versicolor and the like which cannot be digested;
at present, when edible fungi are planted, detection of the environment of a corresponding planting area is carried out through a corresponding sensor detection device, corresponding management staff manually carries out environment regulation and control based on environment data obtained through detection, regional environment detection feedback cannot be achieved, reasonable regulation and control cannot be achieved, evaluation of the overall management and control effect of the planting environment is difficult to achieve, and regulation and control quality and equipment performance analysis of corresponding regulation and control equipment cannot be combined, management of the regulation and control equipment is not facilitated for the management staff, and negative influence is caused on growth of the edible fungi.
Disclosure of Invention
The invention aims to provide an intelligent management and control system for edible fungus planting environments based on artificial intelligence, which solves the problems that the prior art cannot realize the detection feedback and reasonable regulation and control of the regional environment of edible fungus planting areas, cannot realize the evaluation of the overall management and control effect of planting environments, cannot combine the regulation and control quality and the equipment performance analysis of corresponding regulation and control equipment, and is not beneficial to effective management of regulation and control equipment by management staff.
In order to achieve the above purpose, the present invention provides the following technical solutions: an intelligent management and control system for edible fungi planting environments based on artificial intelligence comprises a server, an environment data partition verification module, an equipment regulation and control supervision analysis module, an equipment performance excellence analysis module and an environment management and control quality evaluation module;
the environment data partition checking module divides the edible fungus planting area into a plurality of groups of subareas and marks the subareas as analysis objects i, analyzes the analysis objects i to judge whether the real-time detection data of the corresponding environment management and control project is accurate, and marks the subareas as normal projects or abnormal projects when judging that the real-time data of the corresponding environment management and control project is accurate; the abnormal item of the analysis object i is sent to an equipment regulation and control supervision analysis module through a server;
the equipment regulation and control analysis module acquires regulation and control equipment corresponding to an abnormal item of an analysis object i, carries out regulation and control analysis on the operation of the corresponding regulation and control equipment, generates a regulation and control qualified signal or a regulation and control unqualified signal of the corresponding regulation and control equipment through the regulation and control analysis, and sends the regulation and control unqualified signal and the corresponding regulation and control equipment to the equipment superiority analysis module through a server; the equipment excellence analysis module analyzes the regulation equipment corresponding to the regulation and control unqualified signals, generates the performance unqualified signals or the performance qualified signals corresponding to the regulation and control equipment through analysis, and sends the performance unqualified signals to an environmental equipment monitoring end through a server;
the environment control quality evaluation module is used for dividing the marking information based on the corresponding environment control project of the analysis object i, generating a monitoring normal signal, a high risk monitoring signal or a medium risk monitoring signal of the analysis object i through analysis, analyzing the environment monitoring analysis information of the edible fungus planting area in unit time, generating an environment control qualified signal or an environment control unqualified signal, and transmitting the environment control unqualified signal to the environment equipment monitoring end through the server.
Further, the specific operation process of the environment data partition checking module comprises the following steps:
dividing an edible fungus planting area into a plurality of groups of subareas, marking the corresponding subareas as analysis objects i, i= {1,2, …, n }, wherein n represents the number of subareas and n is a natural number greater than 1; acquiring real-time detection data of a corresponding environment control item of an analysis object i, acquiring real-time performance data of the corresponding environment control item of the analysis object i, performing difference calculation on the corresponding real-time detection data and the real-time performance data to obtain an item detection error value, classifying the corresponding environment control item into a normal item or an abnormal item through environment control item discriminant analysis if the item detection error value does not exceed a preset item detection error value, and judging that the real-time detection data of the corresponding environment control item is inaccurate if the item detection error value exceeds the preset item detection error value.
Further, the specific analysis process of the environmental control project discriminant analysis is as follows:
and carrying out numerical comparison on the real-time detection data of the corresponding environment control item and the corresponding preset item data range, marking the environment control item which is not in the corresponding preset item data range as an abnormal item, if the real-time detection data of the corresponding environment control item is in the corresponding preset item data range, acquiring a deviation value of the corresponding environment control item compared with a limit value of the corresponding preset item data range, acquiring a data deviation shortening speed of the corresponding environment control item, and if the deviation value of the corresponding environment control item compared with the limit value of the corresponding preset item data range does not exceed the corresponding preset deviation value threshold and the corresponding data deviation shortening speed exceeds the corresponding preset deviation shortening speed threshold, marking the corresponding environment control item as the abnormal item, otherwise marking the corresponding environment control item as a normal item.
Further, the specific operation process of the device regulation and control supervision and analysis module comprises the following steps:
obtaining a regulation device corresponding to an abnormal item, calculating a time difference between the starting regulation time of the corresponding regulation device and the abnormal initial time of the corresponding abnormal item to obtain a reaction delay coefficient, marking the time of converting the corresponding environment management item from the abnormal item to the normal item as a regulation success time, calculating the time difference between the regulation success time and the starting regulation time to obtain a regulation efficiency coefficient, and respectively comparing the reaction delay coefficient and the regulation efficiency coefficient with a preset reaction delay coefficient threshold value and a preset regulation efficiency coefficient threshold value in a numerical mode; if the response delay coefficient exceeds a preset response delay coefficient threshold or the regulation efficiency coefficient exceeds a preset regulation efficiency coefficient threshold, generating a regulation supervision disqualification signal;
if the response delay coefficient does not exceed the preset response delay coefficient threshold value and the regulation efficiency coefficient does not exceed the preset regulation efficiency coefficient threshold value, the ending operation time of the corresponding regulation equipment is obtained, the time difference between the ending operation time and the successful regulation time is calculated to obtain the shutdown rationality coefficient, if the shutdown rationality coefficient is within the preset shutdown rationality coefficient range, a regulation and supervision qualified signal is generated, otherwise, a regulation and supervision unqualified signal is generated, the regulation and supervision qualified signal or the regulation and supervision unqualified signal is sent to the server, and the server sends the regulation and supervision unqualified signal to the equipment performance excellence analysis module.
Further, the specific analysis process of the device performance excellence analysis module is as follows:
the method comprises the steps of taking the current moment as an analysis end point, marking the time length of a previous t1 interval of the adjacent analysis end point as an analysis period, obtaining the frequency of a regulation and control unqualified signal generated by corresponding regulation and control equipment in the analysis period, marking the frequency as the unqualified frequency, obtaining the time interval of two groups of adjacent regulation and control unqualified signals, marking the time interval as an unqualified regulation and control interval value, carrying out mean value calculation on all unqualified regulation and control interval values of the corresponding regulation and control equipment in the analysis period to obtain unqualified frequency, carrying out numerical calculation on the unqualified frequency and the unqualified frequency to obtain a time interval abnormal value, generating a performance unqualified signal if the time interval abnormal value of the corresponding regulation and control equipment exceeds a preset time interval abnormal threshold, and carrying out equipment degradation analysis on the corresponding regulation and control equipment if the time interval abnormal value of the corresponding regulation and control equipment does not exceed the preset time interval abnormal threshold.
Further, the specific analysis procedure of the equipment degradation analysis is as follows:
obtaining the equipment service life end date and the equipment start service date of the corresponding regulation equipment, calculating the time difference between the current date and the equipment service life end date to obtain equipment service life time difference, calculating the time difference between the current date and the equipment start service date to obtain equipment service time difference, calculating the ratio of the actual working time length to the idle time length of the corresponding regulation equipment in the interval time length of the equipment service time difference to obtain a working time length occupation ratio, calculating the difference between a preset time period abnormal threshold value and a time period abnormal value to obtain a time period abnormal difference value, carrying out normalization calculation on the equipment service life time difference, the equipment service time difference, the working time length occupation ratio and the time period abnormal difference value of the corresponding regulation equipment to obtain a degradation analysis value, and generating a performance disqualification signal of the corresponding regulation equipment if the degradation analysis value exceeds a preset degradation analysis threshold value, otherwise, generating a performance qualification signal of the corresponding regulation equipment.
Further, the specific operation process of the environment control quality evaluation module comprises the following steps:
acquiring the corresponding environment management item division marking information of the analysis object i, if the analysis object i does not have an abnormal item, generating a monitoring normal signal corresponding to the analysis object i, if the analysis object i has an abnormal item, calculating the ratio of the number of the abnormal items to the number of the normal items to obtain an item abnormal table value, if the item abnormal table value exceeds a preset item abnormal table threshold, generating a high risk monitoring signal corresponding to the analysis object i, otherwise, generating a risk monitoring signal corresponding to the analysis object i;
acquiring the frequency of generating a high-risk monitoring signal, the frequency of a medium-risk monitoring signal and the frequency of monitoring a normal signal of a corresponding analysis object i in unit time, and carrying out numerical calculation on the frequency of the high-risk monitoring signal, the frequency of the medium-risk monitoring signal and the frequency of monitoring the normal signal to obtain a monitoring evaluation value; and carrying out summation calculation on the monitoring evaluation values of all the analysis objects and taking an average value to obtain an evaluation representation value, carrying out variance calculation on the monitoring evaluation values of all the analysis objects to obtain an evaluation deviation value, generating an environment control qualified signal if the evaluation representation value exceeds a preset evaluation representation threshold value and the evaluation deviation value does not exceed the preset evaluation deviation threshold value, generating an environment control disqualification signal if the evaluation representation value does not exceed the preset evaluation representation threshold value and the evaluation deviation value does not exceed the preset evaluation deviation threshold value, and carrying out regional ratio analysis on the rest conditions.
Further, the specific analysis process of the region ratio analysis is as follows:
and carrying out numerical comparison on the monitoring evaluation value of the analysis object i and a preset monitoring evaluation threshold value, marking the corresponding analysis object i as a stable region if the monitoring evaluation value exceeds the preset monitoring evaluation threshold value, marking the corresponding analysis object i as a fault region if the monitoring evaluation value does not exceed the preset monitoring evaluation threshold value, carrying out ratio calculation on the number of the fault regions and the number of the stable regions in unit time to obtain a fault region occupation ratio, and generating an environment control disqualification signal if the fault region occupation ratio exceeds the preset fault region occupation ratio threshold value, otherwise, generating an environment control qualification signal.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the environment data partition checking module is used for carrying out environment partition detection feedback on the edible fungus planting area so as to judge whether the real-time detection data of the corresponding environment management and control project is accurate, the real-time data of the corresponding environment management and control project is marked as a normal project or an abnormal project when being judged to be accurate, the regulation and control equipment corresponding to the abnormal project is subjected to supervision analysis so as to realize the management and control of the regulation and control effect of the regulation and control equipment, the equipment superiority analysis module is used for analyzing the regulation and control equipment corresponding to the regulation and control disqualification signal so as to judge whether the performance of the regulation and control equipment is qualified, so that the effective combination of the regulation and control effect analysis and the performance analysis of the corresponding regulation and control equipment is realized, the elimination and replacement of the corresponding regulation and control equipment is carried out in time, the subsequent corresponding regulation and control effect and regulation and control efficiency are ensured, the growth of edible fungi in the corresponding area is facilitated, and the environment management and control is more intelligent and automatic;
2. according to the invention, the environment control quality evaluation module divides the marking information based on the corresponding environment control project of the analysis object i, generates the monitoring normal signal, the high risk monitoring signal or the medium risk monitoring signal of the analysis object i through analysis, analyzes the environment monitoring analysis information of the edible fungus planting area in unit time, generates the environment control qualified signal or the environment control unqualified signal, and subsequently strengthens the environment supervision of the edible fungus planting area and the maintenance and overhaul of corresponding regulation equipment when the environment equipment supervision receives the environment control unqualified signal, thereby ensuring the growth situation of edible fungi.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
fig. 1 is an overall system block diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one: as shown in fig. 1, the edible fungus planting environment intelligent management and control system based on artificial intelligence provided by the invention comprises a server, an environment data partition checking module, an equipment regulation and control analysis module and an equipment performance excellence analysis module, wherein the server is in communication connection with the environment data partition checking module, the equipment regulation and control analysis module and the equipment performance excellence analysis module, and a processor is in communication connection with an environment equipment monitoring end; the specific analysis process of the environment data partition verification module is as follows:
dividing an edible fungus planting area into a plurality of groups of subareas, marking the corresponding subareas as analysis objects i, i= {1,2, …, n }, wherein n represents the number of subareas and n is a natural number greater than 1; acquiring real-time detection data of a corresponding environment control item (such as environment temperature, environment humidity, environment illumination intensity and the like) of an analysis object i, acquiring real-time performance data of the corresponding environment control item of the analysis object i, performing difference calculation on the corresponding real-time detection data and the real-time performance data to obtain an item detection error value, and judging that the real-time detection data of the corresponding environment control item is inaccurate if the item detection error value exceeds a corresponding preset item detection error value, and timely checking and maintaining related detection sensors by corresponding management personnel if the item detection error value exceeds the corresponding preset item detection error value;
if the item detection error value does not exceed the preset item detection error value, carrying out environmental management and control item discriminant analysis, wherein the method specifically comprises the following steps: comparing the real-time detection data of the corresponding environment control item with the corresponding preset item data range in numerical value, marking the environment control item, of which the real-time detection data is not in the corresponding preset item data range, as an abnormal item, for example, if the corresponding environment control item is the environment temperature, the corresponding preset item data range is the preset temperature data range, when the real-time detection data of the environment temperature is 3 ℃, and the preset temperature data range is 5-20 ℃, the corresponding area environment temperature is unqualified, and indicating that the corresponding area environment temperature is the abnormal item;
if the real-time detection data of the corresponding environment control item is in the corresponding preset item data range, acquiring a deviation value of the corresponding environment control item compared with the threshold value of the corresponding preset item data range, acquiring a data deviation shortening speed of the corresponding environment control item, and if the deviation value of the corresponding environment control item compared with the threshold value of the corresponding preset item data range does not exceed the corresponding preset deviation value threshold value and the corresponding data deviation shortening speed exceeds the corresponding preset deviation shortening speed threshold value, indicating that the normal deviation of the data of the corresponding environment control item in the corresponding area is smaller and still continuously reduced, marking the corresponding environment control item as an abnormal item, otherwise marking the corresponding control item as a normal item.
The environment data partition verification module divides an edible fungus planting area into a plurality of groups of subareas and marks the subareas as analysis objects i, analyzes the analysis objects i to judge whether real-time detection data of corresponding environment management and control items are accurate, marks the subareas as normal items or abnormal items when judging that the real-time data of the corresponding environment management and control items are accurate, and sends the abnormal items of the analysis objects i to the equipment regulation and control supervision analysis module through the server; the equipment regulation and control analysis module acquires regulation and control equipment corresponding to an abnormal item of an analysis object i, carries out regulation and control analysis on the operation of the corresponding regulation and control equipment, generates a regulation and control qualified signal or a regulation and control unqualified signal of the corresponding regulation and control equipment through the regulation and control analysis, and sends the regulation and control unqualified signal and the corresponding regulation and control equipment to the equipment superiority analysis module through a server; the specific analysis process of the supervision analysis is as follows:
obtaining a regulation device corresponding to an abnormal item, calculating a time difference between the starting regulation time of the corresponding regulation device and the abnormal initial time of the corresponding abnormal item to obtain a reaction delay coefficient, marking the time of the corresponding environment management item from the abnormal item to the normal item as a regulation success time, calculating the time difference between the regulation success time and the starting regulation time to obtain a regulation efficiency coefficient, respectively comparing the reaction delay coefficient and the regulation efficiency coefficient with a corresponding preset reaction delay coefficient threshold value and a corresponding preset regulation efficiency coefficient threshold value, and if the reaction delay coefficient exceeds the preset reaction delay coefficient threshold value or the regulation efficiency coefficient exceeds the preset regulation efficiency coefficient threshold value, indicating that the regulation reaction and the regulation efficiency of the corresponding regulation device are abnormal, generating a regulation supervision disqualification signal;
if the response delay coefficient does not exceed the preset response delay coefficient threshold and the regulation efficiency coefficient does not exceed the preset regulation efficiency coefficient threshold, acquiring the end operation time of the corresponding regulation equipment, calculating the time difference between the end operation time and the regulation success time to obtain a shutdown rationality coefficient, comparing the shutdown rationality coefficient with the corresponding preset shutdown rationality coefficient threshold, generating a regulation and control qualified signal if the shutdown rationality coefficient is within the preset shutdown rationality coefficient range, generating a regulation and control unqualified signal if the shutdown rationality coefficient is not within the preset shutdown rationality coefficient range, indicating that the shutdown time of the corresponding regulation equipment is unreasonable, transmitting the regulation and control qualified signal or the regulation and control unqualified signal to a server, timely performing maintenance inspection of the corresponding regulation equipment by corresponding management personnel, and enhancing subsequent regulation and control so as to ensure the subsequent stable and efficient operation of the corresponding regulation equipment, reducing the equipment operation energy consumption and simultaneously ensuring the regulation and control effect, and transmitting the regulation and control unqualified signal to the equipment performance analysis module.
The equipment excellence analysis module analyzes the regulation equipment corresponding to the regulation and control unqualified signals, generates the performance unqualified signals or the performance qualified signals corresponding to the regulation and control equipment through analysis, sends the performance unqualified signals to an environmental equipment monitoring end through a server, and timely performs elimination and replacement of the corresponding regulation and control equipment in the follow-up process when corresponding management staff receive the performance unqualified signals so as to ensure the follow-up corresponding regulation and control effect and regulation and control efficiency, and is helpful for ensuring that edible fungi in a corresponding area grow stably in a proper environment state; the specific analysis process of the equipment performance excellence analysis module is as follows:
the method comprises the steps of taking the current moment as an analysis end point, marking the time length of a previous t1 interval of the adjacent analysis end point as an analysis period, obtaining the frequency of a regulation and control supervision disqualified signal generated by corresponding regulation and control equipment in the analysis period, marking the frequency as disqualified frequency HP, obtaining the time interval of two groups of adjacent regulation and control disqualified signals, marking the time interval as disqualified regulation and control interval value, carrying out mean value calculation on all disqualified regulation and control interval values of the corresponding regulation and control equipment in the analysis period to obtain disqualified frequency HL, and carrying out numerical calculation on the disqualified frequency HP and the disqualified frequency HL through a formula SY=tk1+tk2;
wherein, tk1 and tk2 are preset weight coefficients, and tk1 is more than tk2 is more than 0; in addition, the numerical value of the time period abnormal value SY is in a direct proportion relation with the disqualification frequency HP and the disqualification frequency HL, and the larger the numerical value of the time period abnormal value SY is, the more unstable the regulating operation process of the corresponding regulating equipment is; comparing the abnormal value SY of the time period with the abnormal threshold value of the corresponding preset time period, and generating a performance disqualification signal if the abnormal value of the time period of the corresponding regulation and control equipment exceeds the abnormal threshold value of the preset time period; if the abnormal time period value of the corresponding regulation and control equipment does not exceed the abnormal time period threshold value, carrying out equipment degradation analysis on the corresponding regulation and control equipment, wherein the equipment degradation analysis comprises the following steps of:
acquiring an equipment service life end date and an equipment start service date of corresponding regulation equipment, calculating a time difference between a current date and the equipment service life end date to obtain equipment service life time difference MC, calculating a time difference between the current date and the equipment start service date to obtain equipment service time difference SC, calculating a ratio of actual working time length to idle time length of the corresponding regulation equipment in interval time length of the equipment service time difference to obtain a working time length occupation ratio GZ, and calculating a difference value between a preset time period abnormal threshold value and a time period abnormal value to obtain a time period abnormal difference value YC;
normalizing and calculating the equipment life time difference MC, the equipment use time difference SC, the duty ratio GZ and the time period abnormal difference YC of corresponding regulation equipment through a formula ST=fu1/MC+fu2, SC+fu3, GZ+fu4/YC to obtain a degradation analysis value ST, wherein fu1, fu2, fu3 and fu4 are preset proportionality coefficients, and fu2 is more than 0 and less than fu3 and less than fu1 and fu4; the larger the value of the degradation analysis value ST is, the more the corresponding regulation and control equipment tends to be obsolete; and carrying out numerical comparison on the degradation analysis value ST and a corresponding preset degradation analysis threshold value, if the degradation analysis value ST exceeds the preset degradation analysis threshold value, generating a performance failure signal of the corresponding regulation and control equipment, and if the degradation analysis value ST does not exceed the preset degradation analysis threshold value, generating a performance failure signal of the corresponding regulation and control equipment.
Embodiment two: as shown in fig. 1, the difference between the embodiment and the embodiment 1 is that the server is in communication connection with the environmental control quality evaluation module, the environmental control quality evaluation module divides the marking information based on the corresponding environmental control project of the analysis object i, generates the monitoring normal signal, the high risk monitoring signal or the medium risk monitoring signal of the analysis object i through analysis, analyzes the environmental monitoring analysis information of the edible fungus planting area in unit time, generates the environmental control qualified signal or the environmental control unqualified signal, sends the environmental control unqualified signal to the environmental equipment monitoring end through the server, and subsequently strengthens the environmental monitoring of the edible fungus planting area when the environmental equipment monitoring end receives the environmental control unqualified signal, and strengthens the maintenance and repair of the corresponding regulation equipment, or carries out the replacement of the related regulation equipment according to the requirement, thereby ensuring the growth situation of edible fungi; the specific operation process of the environment control quality evaluation module is as follows:
acquiring corresponding environment management item division marking information of an analysis object i, if the analysis object i does not have an abnormal item, generating a monitoring normal signal corresponding to the analysis object i, if the analysis object i has an abnormal item, calculating the ratio of the number of the abnormal item to the number of the normal item to obtain an item abnormal table value, comparing the item abnormal table value with a preset item abnormal table threshold value, if the item abnormal table value exceeds the preset item abnormal table threshold value, generating a high risk monitoring signal corresponding to the analysis object i, otherwise, generating a stroke risk monitoring signal corresponding to the analysis object i;
acquiring the frequency GJi of the high risk monitoring signal, the frequency ZJi of the medium risk monitoring signal and the frequency CJi of the monitoring normal signal, which correspond to the analysis object i in unit time, and carrying out numerical calculation on the frequency GJi of the high risk monitoring signal, the frequency ZJi of the medium risk monitoring signal and the frequency CJi of the monitoring normal signal by the formula KPi= (a1×CJi)/(a2× GJi +a3× ZJi +0.637) to obtain a monitoring evaluation value KPi; wherein a1, a2 and a3 are preset proportionality coefficients, a1 is more than 1 and a3 is more than 2; and, the larger the value of the monitored evaluation value KPi is, the better the environment control condition of the corresponding analysis object i is;
carrying out summation calculation on the monitoring evaluation values of all the analysis objects and taking an average value to obtain evaluation expression values, carrying out variance calculation on the monitoring evaluation values of all the analysis objects to obtain evaluation deviation values, respectively carrying out numerical comparison on the evaluation expression values and the evaluation deviation values with a preset evaluation expression threshold value and a preset evaluation deviation threshold value, if the evaluation expression values exceed the preset evaluation expression threshold value and the evaluation deviation values do not exceed the preset evaluation deviation threshold value, indicating that the environmental control expression conditions of all the subareas are good and the deviation between the subareas is small, generating an environmental control qualification signal, and if the evaluation expression values do not exceed the preset evaluation expression threshold value and the evaluation deviation values do not exceed the preset evaluation deviation threshold value, indicating that the environmental control expression conditions of all the subareas are poor, namely, generating an environmental control disqualification signal according to the whole edible fungus planting environment;
and in other cases, carrying out numerical comparison on the monitoring evaluation value of the analysis object i and a preset monitoring evaluation threshold value, marking the corresponding analysis object i as a stable region if the monitoring evaluation value exceeds the preset monitoring evaluation threshold value, marking the corresponding analysis object i as a fault region if the monitoring evaluation value does not exceed the preset monitoring evaluation threshold value, carrying out ratio calculation on the number of the fault regions and the number of the stable regions in unit time to obtain a fault region occupation ratio, carrying out numerical comparison on the fault region occupation ratio and a preset fault region occupation ratio value, generating an environment management disqualification signal if the fault region occupation ratio exceeds the preset fault region occupation ratio threshold value, and generating an environment management qualification signal if the fault region occupation ratio does not exceed the preset fault region occupation ratio threshold value.
When the method is used, the environment analysis detection feedback is carried out on the edible fungus planting area through the environment data partition checking module so as to judge whether the real-time detection data of the corresponding environment management and control project is accurate, the real-time detection data of the corresponding environment management and control project is marked as a normal project or an abnormal project when the real-time data of the corresponding environment management and control project is judged to be accurate, the equipment regulation and control monitoring analysis module carries out monitoring analysis on the regulation and control equipment corresponding to the abnormal project of the analysis object i so as to generate a regulation and control qualified signal or a regulation and control unqualified signal of the corresponding regulation and control equipment, the equipment superiority analysis module analyzes the regulation and control equipment corresponding to the regulation and control unqualified signal so as to judge whether the performance of the regulation and control equipment is qualified, so that the effective combination of the regulation and control effect analysis and the performance analysis of the corresponding regulation and control equipment is carried out in time after the corresponding manager receives the performance unqualified signal, the follow-up corresponding regulation and control effect and regulation and control efficiency are guaranteed, the growth of edible fungus in the corresponding area is facilitated to be ensured, and the environment management and control is more intelligent and automatic.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation. The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. An intelligent management and control system for edible fungi planting environments based on artificial intelligence is characterized by comprising a server, an environment data partition verification module, an equipment regulation and control supervision analysis module, an equipment performance excellence analysis module and an environment management and control quality evaluation module;
the environment data partition checking module divides the edible fungus planting area into a plurality of groups of subareas and marks the subareas as analysis objects i, analyzes the analysis objects i to judge whether the real-time detection data of the corresponding environment management and control project is accurate, and marks the subareas as normal projects or abnormal projects when judging that the real-time data of the corresponding environment management and control project is accurate; the abnormal item of the analysis object i is sent to an equipment regulation and control supervision analysis module through a server;
the equipment regulation and control analysis module acquires regulation and control equipment corresponding to an abnormal item of an analysis object i, carries out regulation and control analysis on the operation of the corresponding regulation and control equipment, generates a regulation and control qualified signal or a regulation and control unqualified signal of the corresponding regulation and control equipment through the regulation and control analysis, and sends the regulation and control unqualified signal and the corresponding regulation and control equipment to the equipment superiority analysis module through a server; the equipment excellence analysis module analyzes the regulation equipment corresponding to the regulation and control unqualified signals, generates the performance unqualified signals or the performance qualified signals corresponding to the regulation and control equipment through analysis, and sends the performance unqualified signals to an environmental equipment monitoring end through a server;
the environment control quality evaluation module is used for dividing the marking information based on the corresponding environment control project of the analysis object i, generating a monitoring normal signal, a high risk monitoring signal or a medium risk monitoring signal of the analysis object i through analysis, analyzing the environment monitoring analysis information of the edible fungus planting area in unit time, generating an environment control qualified signal or an environment control unqualified signal, and transmitting the environment control unqualified signal to the environment equipment monitoring end through the server.
2. The intelligent management and control system for edible fungi planting environment based on artificial intelligence according to claim 1, wherein the specific operation process of the environment data partition verification module comprises the following steps:
dividing an edible fungus planting area into a plurality of groups of subareas, marking the corresponding subareas as analysis objects i, i= {1,2, …, n }, wherein n represents the number of subareas and n is a natural number greater than 1; acquiring real-time detection data of a corresponding environment control item of an analysis object i, acquiring real-time performance data of the corresponding environment control item of the analysis object i, performing difference calculation on the corresponding real-time detection data and the real-time performance data to obtain an item detection error value, classifying the corresponding environment control item into a normal item or an abnormal item through environment control item discriminant analysis if the item detection error value does not exceed a preset item detection error value, and judging that the real-time detection data of the corresponding environment control item is inaccurate if the item detection error value exceeds the preset item detection error value.
3. The intelligent management and control system for edible fungi planting environment based on artificial intelligence according to claim 2, wherein the specific analysis process of the environmental management and control project discriminant analysis is as follows:
and carrying out numerical comparison on the real-time detection data of the corresponding environment control item and the corresponding preset item data range, marking the environment control item which is not in the corresponding preset item data range as an abnormal item, if the real-time detection data of the corresponding environment control item is in the corresponding preset item data range, acquiring a deviation value of the corresponding environment control item compared with a limit value of the corresponding preset item data range, acquiring a data deviation shortening speed of the corresponding environment control item, and if the deviation value of the corresponding environment control item compared with the limit value of the corresponding preset item data range does not exceed the corresponding preset deviation value threshold and the corresponding data deviation shortening speed exceeds the corresponding preset deviation shortening speed threshold, marking the corresponding environment control item as the abnormal item, otherwise marking the corresponding environment control item as a normal item.
4. The intelligent management and control system for edible fungi planting environment based on artificial intelligence according to claim 1, wherein the specific operation process of the equipment regulation and control supervision and analysis module comprises the following steps:
obtaining a regulation device corresponding to an abnormal item, calculating a time difference between the starting regulation time of the corresponding regulation device and the abnormal initial time of the corresponding abnormal item to obtain a reaction delay coefficient, marking the time of converting the corresponding environment management item from the abnormal item to the normal item as a regulation success time, calculating the time difference between the regulation success time and the starting regulation time to obtain a regulation efficiency coefficient, and respectively comparing the reaction delay coefficient and the regulation efficiency coefficient with a preset reaction delay coefficient threshold value and a preset regulation efficiency coefficient threshold value in a numerical mode; if the response delay coefficient exceeds a preset response delay coefficient threshold or the regulation efficiency coefficient exceeds a preset regulation efficiency coefficient threshold, generating a regulation supervision disqualification signal;
if the response delay coefficient does not exceed the preset response delay coefficient threshold value and the regulation efficiency coefficient does not exceed the preset regulation efficiency coefficient threshold value, the ending operation time of the corresponding regulation equipment is obtained, the time difference between the ending operation time and the successful regulation time is calculated to obtain the shutdown rationality coefficient, if the shutdown rationality coefficient is within the preset shutdown rationality coefficient range, a regulation and supervision qualified signal is generated, otherwise, a regulation and supervision unqualified signal is generated, the regulation and supervision qualified signal or the regulation and supervision unqualified signal is sent to the server, and the server sends the regulation and supervision unqualified signal to the equipment performance excellence analysis module.
5. The intelligent management and control system for edible fungi planting environment based on artificial intelligence according to claim 4, wherein the specific analysis process of the equipment performance excellence analysis module is as follows:
the method comprises the steps of taking the current moment as an analysis end point, marking the time length of a previous t1 interval of the adjacent analysis end point as an analysis period, obtaining the frequency of a regulation and control unqualified signal generated by corresponding regulation and control equipment in the analysis period, marking the frequency as the unqualified frequency, obtaining the time interval of two groups of adjacent regulation and control unqualified signals, marking the time interval as an unqualified regulation and control interval value, carrying out mean value calculation on all unqualified regulation and control interval values of the corresponding regulation and control equipment in the analysis period to obtain unqualified frequency, carrying out numerical calculation on the unqualified frequency and the unqualified frequency to obtain a time interval abnormal value, generating a performance unqualified signal if the time interval abnormal value of the corresponding regulation and control equipment exceeds a preset time interval abnormal threshold, and carrying out equipment degradation analysis on the corresponding regulation and control equipment if the time interval abnormal value of the corresponding regulation and control equipment does not exceed the preset time interval abnormal threshold.
6. The intelligent management and control system for edible fungi planting environment based on artificial intelligence according to claim 5, wherein the specific analysis process of equipment degradation analysis is as follows:
obtaining the equipment service life end date and the equipment start service date of the corresponding regulation equipment, calculating the time difference between the current date and the equipment service life end date to obtain equipment service life time difference, calculating the time difference between the current date and the equipment start service date to obtain equipment service time difference, calculating the ratio of the actual working time length to the idle time length of the corresponding regulation equipment in the interval time length of the equipment service time difference to obtain a working time length occupation ratio, calculating the difference between a preset time period abnormal threshold value and a time period abnormal value to obtain a time period abnormal difference value, carrying out normalization calculation on the equipment service life time difference, the equipment service time difference, the working time length occupation ratio and the time period abnormal difference value of the corresponding regulation equipment to obtain a degradation analysis value, and generating a performance disqualification signal of the corresponding regulation equipment if the degradation analysis value exceeds a preset degradation analysis threshold value, otherwise, generating a performance qualification signal of the corresponding regulation equipment.
7. The intelligent management and control system for edible fungi planting environment based on artificial intelligence according to claim 1, wherein the specific operation process of the environment management and control quality evaluation module comprises the following steps:
acquiring the corresponding environment management item division marking information of the analysis object i, if the analysis object i does not have an abnormal item, generating a monitoring normal signal corresponding to the analysis object i, if the analysis object i has an abnormal item, calculating the ratio of the number of the abnormal items to the number of the normal items to obtain an item abnormal table value, if the item abnormal table value exceeds a preset item abnormal table threshold, generating a high risk monitoring signal corresponding to the analysis object i, otherwise, generating a risk monitoring signal corresponding to the analysis object i;
acquiring the frequency of generating a high-risk monitoring signal, the frequency of a medium-risk monitoring signal and the frequency of monitoring a normal signal of a corresponding analysis object i in unit time, and carrying out numerical calculation on the frequency of the high-risk monitoring signal, the frequency of the medium-risk monitoring signal and the frequency of monitoring the normal signal to obtain a monitoring evaluation value; and carrying out summation calculation on the monitoring evaluation values of all the analysis objects and taking an average value to obtain an evaluation representation value, carrying out variance calculation on the monitoring evaluation values of all the analysis objects to obtain an evaluation deviation value, generating an environment control qualified signal if the evaluation representation value exceeds a preset evaluation representation threshold value and the evaluation deviation value does not exceed the preset evaluation deviation threshold value, generating an environment control disqualification signal if the evaluation representation value does not exceed the preset evaluation representation threshold value and the evaluation deviation value does not exceed the preset evaluation deviation threshold value, and carrying out regional ratio analysis on the rest conditions.
8. The intelligent management and control system for edible fungi planting environment based on artificial intelligence as set forth in claim 7, wherein the specific analysis process of the area ratio analysis is as follows:
and carrying out numerical comparison on the monitoring evaluation value of the analysis object i and a preset monitoring evaluation threshold value, marking the corresponding analysis object i as a stable region if the monitoring evaluation value exceeds the preset monitoring evaluation threshold value, marking the corresponding analysis object i as a fault region if the monitoring evaluation value does not exceed the preset monitoring evaluation threshold value, carrying out ratio calculation on the number of the fault regions and the number of the stable regions in unit time to obtain a fault region occupation ratio, and generating an environment control disqualification signal if the fault region occupation ratio exceeds the preset fault region occupation ratio threshold value, otherwise, generating an environment control qualification signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310592477.2A CN116579874A (en) | 2023-05-24 | 2023-05-24 | Edible fungus planting environment intelligent management and control system based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310592477.2A CN116579874A (en) | 2023-05-24 | 2023-05-24 | Edible fungus planting environment intelligent management and control system based on artificial intelligence |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116579874A true CN116579874A (en) | 2023-08-11 |
Family
ID=87543856
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310592477.2A Pending CN116579874A (en) | 2023-05-24 | 2023-05-24 | Edible fungus planting environment intelligent management and control system based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116579874A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116805530A (en) * | 2023-08-23 | 2023-09-26 | 领华数科(厦门)信息技术有限公司 | Medical equipment use efficiency assessment method based on artificial intelligence |
| CN116974313A (en) * | 2023-09-22 | 2023-10-31 | 北京万通益生物科技有限公司 | Drying chamber temperature control system suitable for lactobacillus thermal dry production |
| CN117055668A (en) * | 2023-10-13 | 2023-11-14 | 山西聚源生物科技有限公司 | Edible fungus industrial production environment control system |
| CN117540934A (en) * | 2024-01-08 | 2024-02-09 | 山东科翔智能科技有限公司 | Intelligent monitoring system for wheat growth period based on data analysis |
| CN117590788A (en) * | 2023-12-18 | 2024-02-23 | 广州市广味源食品有限公司 | Soybean sauce production workshop environment monitoring system based on sterilization control |
| CN117746345A (en) * | 2024-02-21 | 2024-03-22 | 国网山西省电力公司物资分公司 | Material state monitoring system and method based on big data |
| CN117590788B (en) * | 2023-12-18 | 2024-05-31 | 广州市广味源食品有限公司 | Soybean sauce production workshop environment monitoring system based on sterilization control |
-
2023
- 2023-05-24 CN CN202310592477.2A patent/CN116579874A/en active Pending
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116805530A (en) * | 2023-08-23 | 2023-09-26 | 领华数科(厦门)信息技术有限公司 | Medical equipment use efficiency assessment method based on artificial intelligence |
| CN116974313A (en) * | 2023-09-22 | 2023-10-31 | 北京万通益生物科技有限公司 | Drying chamber temperature control system suitable for lactobacillus thermal dry production |
| CN116974313B (en) * | 2023-09-22 | 2023-12-29 | 北京万通益生物科技有限公司 | Drying chamber temperature control system suitable for lactobacillus thermal dry production |
| CN117055668A (en) * | 2023-10-13 | 2023-11-14 | 山西聚源生物科技有限公司 | Edible fungus industrial production environment control system |
| CN117055668B (en) * | 2023-10-13 | 2023-12-12 | 山西聚源生物科技有限公司 | Edible fungus industrial production environment control system |
| CN117590788A (en) * | 2023-12-18 | 2024-02-23 | 广州市广味源食品有限公司 | Soybean sauce production workshop environment monitoring system based on sterilization control |
| CN117590788B (en) * | 2023-12-18 | 2024-05-31 | 广州市广味源食品有限公司 | Soybean sauce production workshop environment monitoring system based on sterilization control |
| CN117540934A (en) * | 2024-01-08 | 2024-02-09 | 山东科翔智能科技有限公司 | Intelligent monitoring system for wheat growth period based on data analysis |
| CN117540934B (en) * | 2024-01-08 | 2024-04-05 | 山东科翔智能科技有限公司 | Intelligent monitoring system for wheat growth period based on data analysis |
| CN117746345A (en) * | 2024-02-21 | 2024-03-22 | 国网山西省电力公司物资分公司 | Material state monitoring system and method based on big data |
| CN117746345B (en) * | 2024-02-21 | 2024-04-23 | 国网山西省电力公司物资分公司 | Material state monitoring system and method based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116579874A (en) | Edible fungus planting environment intelligent management and control system based on artificial intelligence | |
| CN112906735B (en) | Domestic fungus environment big data detecting system | |
| CN109086928B (en) | Photovoltaic power station real-time power prediction method based on SAGA-FCM-LSSVM model | |
| CN111680820B (en) | Distributed photovoltaic power station fault diagnosis method and device | |
| US20230315062A1 (en) | Industrial internet of things, mthods and mediums for early warning of descending function fault of equipment | |
| CN115185220A (en) | Agriculture and forestry disease and pest monitoring system based on thing networking | |
| CN116627082A (en) | Intelligent management system for live pig breeding suitable for stock quantity checking | |
| CN111414586A (en) | Environment monitoring method and system | |
| CN115938083B (en) | Agricultural monitoring and early warning method and system based on mobile terminal | |
| CN113887749A (en) | Cloud edge cooperation-based multi-dimensional monitoring and disposal method, device and platform for power internet of things | |
| CN115204635A (en) | Agricultural greenhouse production management system based on big data analysis | |
| CN116562623A (en) | Real-time planting risk assessment system suitable for velvet mushroom is planted | |
| CN117151631A (en) | Construction progress monitoring and management system based on wisdom building site | |
| CN117391427A (en) | Medical instrument production intelligent supervision system based on artificial intelligence | |
| CN115342859A (en) | Multifunctional grain condition detection system and detection method | |
| CN115328233A (en) | Warmhouse booth environment intelligent regulation management system | |
| CN115454176A (en) | Wisdom green house ventilation control system based on thing networking | |
| CN113456063B (en) | Artificial intelligence-based dead chicken disease detection system and detection method | |
| CN117350525A (en) | Crop growth data management decision-making system based on artificial intelligence | |
| CN115361424B (en) | Medical equipment monitoring method and system based on data analysis of Internet of things | |
| CN116470491A (en) | Photovoltaic power probability prediction method and system based on copula function | |
| CN115034617A (en) | Cloud computing system for agricultural information integration | |
| CN114967628A (en) | Health food production management and control system that disinfects based on 5G intelligence mill | |
| CN116302763B (en) | Touch detection method and system for Micro LED display screen | |
| CN116668256B (en) | Real-time operation influence early warning control system suitable for communication equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |